Decision tree or how to find segments that can expand 2 times
Decision tree or how to find segments that can expand 2 times
How to develop a category, which products are priority in it, and which should leave the shelf space?
Any changes in assortment and layout must be verified, based on mathematical calculations and models. Today we will consider a decision tree (DT) as an effective tool in category management. Do you want to know how to use the decision tree correctly and see examples of cases of implementing in practice? All the fun in one article. So, Let`s go!
The study and construction of a decision tree gives an understanding of which segments of the category should be developed, and on what basis the client makes a choice.
Decision tree in category management
In fact, choosing a product is a complex process that takes place already at the store shelf. We consider the buyer's decision tree as a powerful analytics tool that allows you to get unexpected insights for developing a strategy both separately in category management and for the entire retail chain.
The task of studying a decision tree in category management is to develop narrow segments of goods that our specific customer comes for. How it works? There are buyers who come to the store for products of a certain category and price range. It is advisable to place such SKUs side by side when displaying so that the buyer does not look for it and can make his choice with comfort. This combination of SKUs often has a positive effect on the development of the category.
In fact, demand switching baskets are formed, that is, products between which a choice is made. That is why decision tree analysis is important when compiling planograms and optimizing calculations. Read on for the benefits of mathematical algorithms and decision tree methods.
7 reasons to use decision trees in retail
The stores of different retailers have their own visitors, so each retailer has its own decision tree. Big data analysis helps a retailer understand and digitize a purchasing decision tree. In their practice, Consulting for Retail (C4R) specialists often use DT. Moreover, to build a decision tree, experts do not use a survey and observations, but the process of studying the history of purchases through loyalty cards, as well as analyzing which products within a category the buyer makes a choice and why.
The specialists of the C4R team maximally form the DTs for the buyer of a certain network. This allows the retailer to create a targeted offer for its target customer and meet the demand.
Would you like to learn how Num8erz analytical tools work on case studies of large retailers and manufacturers? Watch the presentation on how Num8erz.InsightsPanel helps to analyze the switching of demand from a well-known juice brand and the launch of a new beverage. Click to download the presentation! If you have any questions, please write to [email protected] or fill out the consultation request form.
7 Key Benefits of Analyzing and Applying a DTs:
- Simplifies the choice of the buyer - allows you to group products in the way the visitor is looking for them.
- Increases demand and helps to understand which sub-segment is worth developing by identifying demand switching baskets with trending growth.
- Allows you to form targeted marketing offers to customers: gives an answer on how to choose an assortment for these promotions.
- Gives an understanding of which products are competing with each other and how to strengthen the position of a particular product relative to competitors - important information for both the retailer and the supplier.
- Helps to do the analysis of demand switching, which in turn explains the processes of cannibalization.
- Allows you to define the boundaries of demand, the saturation of the category and demand switching baskets. And that's why it matters. For example, the category of wines itself can be extensive, and the receipts show that the buyer's choice boils down to three to five persistent names. By strengthening these demand switching baskets, we can get a good indicator of sales growth. Although a superficial assessment might have thought that the entire category of wines should be reduced.
- It helps to understand the units of buyers' needs: it so happens that a segment of a category is sold poorly, but removing it from the assortment, there is a risk of losing buyers who was covering their need by purchasing these products.
The C4R team uses a specialized service for in-depth analysis of the history of purchases using loyalty cards in the retail network. The team's experience in auditing and consulting retail business processes, as well as in implementing IT projects allows you to get the most out of the tool. For understanding, let's consider the process of working with DTs.
How do you get valuable insights from descision tree analysis?
- Customers return to the store and make purchases with loyalty cards, each time choosing certain products. Looking through the history of purchases, our task is to find some patterns. In the method of working with a decision tree, there are three main stages.
- Customers return to the store and make purchases with loyalty cards, each time choosing certain products. Looking through the history of purchases, our task is to find some patterns. In the method of working with a decision tree, there are three main stages.
- Finding similarities / patterns. We track what product the customer comes to the store for, what product he chooses. The analysis allows you to determine which customers have the same product groups in their carts. All information is in the receipts, the main thing is to work with it. Our task is to determine the groups of goods between which the buyer makes a choice.
Interpretation of data. At this stage, it is important to correctly explain why the choice is made between these products. It can be just a group of products that have a beautiful label, or products from the same price segment. Either they are wines of a certain type, brand, or they are united by the fact that they are included in the TOP list of the "wine" application in a smartphone.
Confirmation of assumptions. Collaborating with Category Managers is important at this stage, as they know the product subgroups best and help explain their similarities.
Based on the data obtained during the analysis, hierarchical clustering is built. What results does decision tree analysis bring in practice? About this in the next paragraph!
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A practical case is a prime example of how decision tree analysis works
Decision trees are widely used in retail, so there are many successful cases. And here are a couple of them!
Case # 1: How to find segments that can expand 2 times
In the stores of a large retail chain located in the city center, a pattern was determined in the sale of coffee. The group of coffee was not alike, it was united only by the fact that the coffee had an Italian name. Examining the category in more detail, we determined that it has considerable potential for expansion. It turned out that there were many office buildings in the microdistrict and office managers bought coffee with a name that they associated with the country of origin of the delicious high-quality drink. By adding drinks with an Italian name, the coffee category has been expanded. This approach doubled the sales in this demand switching basket.
Case # 2: How We Increased the Growth of the Beer Category to 27%
Case of the C4R team in a large retail chain in Central Asia. In certain stores located in expensive areas of residence, we found a strong attachment of a group of visitors to imported beer. The development of narrow segments in imported beer led to an increase in the category by 27%.
When a visitor comes to a store, the retail chain must offer what he needs and not waste its resources on storing, ordering and servicing unclaimed goods. What categories have prospects? Decision tree analysis from C4R specialists helps determine this.
Want to learn more about audit and consulting services for retail, as well as systems for automating category management processes, pricing, ADR solutions and ERP implementation in retail? Dial +38 (044) 492-67-00 or fill out the feedback form. Our experts will contact you.
The author of the article is Andrey Shevchuk,
Head of Business Consulting C4R,
